Informatics for health & social care最新文献

{"title":"Enhancing health monitoring in smart offices: a multi-layered digital twin approach.","authors":"Ankush Manocha, Munish Bhatia, Sandeep Kumar Sood","doi":"10.1080/17538157.2026.2623489","DOIUrl":"https://doi.org/10.1080/17538157.2026.2623489","url":null,"abstract":"<p><p>Sedentary nature of office work contributes to a range of physical health issues, including obesity, which can result from prolonged inactivity, and cardiovascular diseases, linked to heightened risk factors associated with a lack of movement. Furthermore, extended periods of sitting can lead to musculoskeletal disorders, causing discomfort and injuries related to poor posture and ergonomics. Collectively, these factors underscore the profound negative impact of sedentary behavior in the workplace on overall well-being. To address these issues, this study proposes a multi-layered digital twin (DT) system for remote healthcare monitoring in a smart office setting. The suggested approach thoroughly investigates various office-related actions in a DT environment, rating their criticality to estimate potential health consequences. By mining temporal instances of these events, a Physiological Risk Index (PRI) is derived, supporting a predictive healthcare framework capable of generating automated alerts during health emergencies. Furthermore, the time-based data module is designed to assist healthcare practitioners in making better decisions by providing precise information about significant occurrences. The system's usability and efficacy are demonstrated by testing it against two challenging datasets obtained from internet repositories. The findings indicate that the proposed approach is both efficient and effective in creating a comprehensive medical system.</p>","PeriodicalId":101409,"journal":{"name":"Informatics for health & social care","volume":" ","pages":"1-22"},"PeriodicalIF":2.4,"publicationDate":"2026-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146215423","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An overview of Parkinson's disease: a narrative review.","authors":"Hiba Shamweel, Shamweel Ahmad","doi":"10.1080/17538157.2026.2627309","DOIUrl":"https://doi.org/10.1080/17538157.2026.2627309","url":null,"abstract":"<p><p>Parkinson's disease (PD) is a degenerative neurological condition defined by a wide range of motor and non-motor symptoms that can affect function to varying degrees. One of the hallmarks of PD is postural instability (PI), along with rest tremor, stiffness, and bradykinesia. PI is a primary cause of disability and decreased quality of life in people with PD. This paper presents a review of the available literature to understand PD, its pathophysiology, diagnosis, various risk factors and its pharmacological and non-pharmacological management. This review offers an overview of physiotherapeutic methods and recent advances in PD care, highlighting the growing role of digital health technologies, artificial intelligence (AI), and innovative rehabilitation tools in improving diagnosis, treatment, and patient outcomes. Diagnosis of PD relies on clinical signs observed during history-taking and physical examination, along with motor fluctuations and response to dopamine therapies over time. Identifying risk factors specific to PI helps in diagnosis and prevention by differentiating between modifiable and non-modifiable causes. The management of PD includes both pharmacological and non-pharmacological approaches. Physiotherapy is a common therapeutic approach for managing symptoms in patients with PD. Management of PD has seen various improvements with the emerging technologies, with ongoing studies driving further advancements.</p>","PeriodicalId":101409,"journal":{"name":"Informatics for health & social care","volume":" ","pages":"1-11"},"PeriodicalIF":2.4,"publicationDate":"2026-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146204579","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Toward an explainable AI-Based clinical decision support system for predicting adverse outcomes in Rhabdomyolysis.","authors":"Fulden Cantaş Türkiş, Bugra Varol, Yalcin Golcuk, Ömer Faruk Karakoyun","doi":"10.1080/17538157.2026.2628838","DOIUrl":"https://doi.org/10.1080/17538157.2026.2628838","url":null,"abstract":"<p><p>Rhabdomyolysis is a severe condition with high morbidity and mortality, driven by complications like acute kidney injury. Early risk stratification remains challenging as traditional scores fail to capture complex data patterns. This study lays the foundation for an explainable AI (XAI)-based clinical decision support system (CDSS) by developing a machine learning model to predict the composite outcome of renal replacement therapy or 90-day mortality. Using routinely available admission data from 1031 adults, we applied multivariate imputation, Boruta feature selection, and ADASYN for class imbalance. The CatBoost model achieved the highest discrimination (AUC = 0.942, 95% CI: 0.904-0.980), accuracy (0.913), and maintained good calibration (Brier score = 0.080). Shapley additive explanations (SHAP) identified creatinine, troponin T, and albumin as key predictors, validating clinical plausibility and enabling instance-level explanations for CDSS deployment. Decision-curve analysis confirmed superior net benefit against treat-all or treat-none strategies across clinically relevant thresholds. We propose a framework for integrating this interpretable model into electronic health records to provide real-time risk scores at the point of care. Calibration drift in older adults highlights the need for age-specific refinement, underscoring the value of transparent, evaluable AI in clinical informatics.</p>","PeriodicalId":101409,"journal":{"name":"Informatics for health & social care","volume":" ","pages":"1-15"},"PeriodicalIF":2.4,"publicationDate":"2026-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146204614","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Modeling localized thermal therapy through coupled heat transfer and metabolic dynamics.","authors":"Sabira Ali, Sumaira Anjum, Mir Aijaz, M A Khanday","doi":"10.1080/17538157.2026.2622494","DOIUrl":"https://doi.org/10.1080/17538157.2026.2622494","url":null,"abstract":"<p><p>Local hyperthermia is a noninvasive treatment that applies controlled heating to damage tumor cells, limit proliferation, and enhance therapy. This study models temperature distribution in tumor-bearing tissue to quantify heat-induced damage and examines its effects on tumor metabolism and glucose diffusion. Using Pennes' Bioheat equation, temperature variations in finite tissue are computed and integrated into a modified tumor growth model that includes thermal damage. The bioheat equation is solved with the finite difference method, while tumor progression under temperatures from 39°C to 43°C is simulated using Euler's method. Numerical results are validated against existing tumor and glucose concentration models. The findings show that higher hyperthermia temperatures increase thermal damage, reduce glucose availability, and significantly inhibit tumor growth, leading to shrinkage over time. The study highlights the importance of incorporating thermal effects into tumor-growth models and provides a computational framework for improving hyperthermia-based cancer therapy.</p>","PeriodicalId":101409,"journal":{"name":"Informatics for health & social care","volume":" ","pages":"1-13"},"PeriodicalIF":2.4,"publicationDate":"2026-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146204642","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Determining the intersection of health literacy and healthy lifestyle behaviors: comparative analysis of individuals with and without COVID-19.","authors":"Ezgi Türkmen, Nilay Arman, Bünyamin Bulut, Elif Yiğit, Melike Murutoğlu, Sevda Yulu","doi":"10.1080/17538157.2026.2621309","DOIUrl":"https://doi.org/10.1080/17538157.2026.2621309","url":null,"abstract":"<p><strong>Objective: </strong>Investigating whether there is a difference in health literacy and healthy lifestyle behaviors between individuals who have and have not had infection after the COVID-19 outbreak, and also to examine the relationship between health literacy and healthy lifestyle behaviors.</p><p><strong>Methods: </strong>Participants were assessed with the European Health Literacy Survey (HLS-EU-Q) and the Healthy Lifestyle Profile-II (HPLP-II) scales online via Google Forms using various social media platforms. Also, the individuals' habits of obtaining health-related knowledge and general health conditions, health-related recourse sources, and the frequency of exercise were also recorded and compared.</p><p><strong>Results: </strong>Three hundred and sixty-seven individuals (64.4% female) participated in the study and analysis of the HLS-EU-Q and HPLP-II Scale results of all participants revealed a positive correlation between two scales (<i>p</i> = .009, <i>r</i> = 0.137). However, no significant difference was found between participants with (Group I, <i>n</i> = 183) and without (Group II, <i>n</i> = 184) COVID-19 infection in terms of HLS-EU-Q and HPLP-II scales (<i>p</i> > .05).</p><p><strong>Conclusion: </strong>It was concluded that the health literacy levels and healthy lifestyle behaviors of individuals who had and did not have COVID-19 infection were similar. However, there was a weak but significant correlation between the level of health literacy and the adoption of healthy lifestyle behaviors.</p>","PeriodicalId":101409,"journal":{"name":"Informatics for health & social care","volume":" ","pages":"1-11"},"PeriodicalIF":2.4,"publicationDate":"2026-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146138175","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploring barriers to adoption of telemedicine platforms in rural Nigerian communities.","authors":"Akinade Adebowale Adewojo, Precious Olalere","doi":"10.1080/17538157.2026.2614661","DOIUrl":"https://doi.org/10.1080/17538157.2026.2614661","url":null,"abstract":"<p><p>Telemedicine holds significant potential to transform healthcare delivery in rural Nigerian communities; however, its adoption remains constrained by various barriers. This study explores patient-level barriers, including technological access, health literacy, and cultural attitudes, as well as provider-level challenges such as training, infrastructure, and organizational support. Additionally, it investigates the role of socio-economic factors and community dynamics in shaping perceptions of telemedicine. Using qualitative interviews with 39 participants (24 patients and 15 providers), the study identifies key obstacles, including the high cost of devices and internet data, unreliable connectivity, low health literacy, and cultural stigma surrounding remote healthcare consultations. The findings align with Rogers' Diffusion of Innovation theory, revealing that barriers related to relative advantage, complexity, and compatibility significantly affect the adoption process. Strategies to mitigate these barriers include subsidizing technology costs, implementing digital literacy programs, providing culturally tailored telemedicine models, and ensuring better infrastructure and technical support for healthcare providers. Recommendations also emphasize the importance of community-centered engagement to enhance acceptance and usage. This study highlights the need for systemic interventions and participatory approaches to design context-specific solutions that address both patient and provider challenges. Future research should focus on participatory action research to develop and implement effective interventions for telemedicine adoption in rural areas.</p>","PeriodicalId":101409,"journal":{"name":"Informatics for health & social care","volume":" ","pages":"1-14"},"PeriodicalIF":2.4,"publicationDate":"2026-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146044517","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Effective lung nodule segmentation and classification by employing a SPPUNet model and global context attention-based InceptionV3.","authors":"Krishna Kumari L, Rajalakshmi R","doi":"10.1080/17538157.2025.2611342","DOIUrl":"https://doi.org/10.1080/17538157.2025.2611342","url":null,"abstract":"<p><p>Lung nodules (LNs) detection using computer tomography (CT) images is essential to reduce the mortality of lung cancer (LC). The complex three-dimensional structure of lung CT data and the variation in the forms and appearances of LNs make the accurate identification of pulmonary nodules still extremely challenging. Although deep learning (DL) methods outperform handcrafted approaches, several challenges remain to be solved. Detecting malignant regions alone is insufficient for clinical decision-making; segmentation by severity and grading analysis are necessary to reduce false positives. Training DL models also requires many datasets, which is difficult in the medical domain due to ethical concerns, limited expert annotations, and the scarcity of disease-specific images. Moreover, insufficient data and class imbalance often lead to overfitting and a reduction in performance. To address these limitations, this study proposes a hybrid pre-trained architecture for more accurate automated pulmonary nodule segmentation and classification. The research comprises 3 phases: preprocessing, segmentation, and classification. Initially, the CT lung images are gathered from the openly available LUNA16 dataset. Then, preprocessing is performed on the collected data by noise filtering using a guided filter and data augmentation to balance the dataset. Doing so improves the data quality for learning and classification processes and prevents the model from biased outcomes. Afterward, the Spatial Pyramid Pooling centered U-shaped network (SPPUNet) is employed to segment the lung regions, enabling the classifier to easily identify and analyze nodules, lesions, and other abnormalities. Finally, the classification is performed using the Global Context Attention integrated InceptionV3 (GCAINCPV3) network, which enables medical professionals to determine the nature of the nodule and provide the most appropriate treatment plan for patients. The outcomes demonstrated that the proposed system outperforms existing systems, achieving an accuracy of 99.23%.</p>","PeriodicalId":101409,"journal":{"name":"Informatics for health & social care","volume":" ","pages":"1-23"},"PeriodicalIF":2.4,"publicationDate":"2026-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146021255","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Correction.","authors":"","doi":"10.1080/17538157.2025.2610145","DOIUrl":"10.1080/17538157.2025.2610145","url":null,"abstract":"","PeriodicalId":101409,"journal":{"name":"Informatics for health & social care","volume":" ","pages":"i-iii"},"PeriodicalIF":2.4,"publicationDate":"2026-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146014121","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A chronic kidney disease prediction system based on Internet of Things using walrus optimized deep learning technique.","authors":"Sakthimohan M, Thenmozhi M, Elizabeth Rani G, Arun M","doi":"10.1080/17538157.2025.2610695","DOIUrl":"https://doi.org/10.1080/17538157.2025.2610695","url":null,"abstract":"<p><p>The Internet of Things (IoT) and cloud computing (CC) concepts are commonly incorporated in healthcare applications. In the healthcare industry, a huge quantity of patient data is generated by IoT devices. The integral storage of mobile devices and processing power is used to analyze the stored data in the cloud. The Internet of Medical Things (IoMT) combines health monitoring mechanisms with medical equipment and sensors to monitor patient records and offer extra smart and experienced healthcare services. This paper proposes an effective and walrus-optimized deep learning (DL) technique for chronic kidney disease (CKD) prediction in IoT. To begin, the data are collected from the CKD dataset, and the preprocessing procedures, such as missing value imputation, numerical conversion, and normalization, are performed to improve the quality of the dataset. Then, dataset balancing is done using the k-means (KM) clustering algorithm to prevent the model from making inaccurate predictions. After that, enhanced residual network 50 (EResNet50) is utilized to extract more discriminative features from the dataset. From that, the optimal features are selected via elite opposition and the Cauchy distribution-based walrus optimization algorithm (ECWOA). Finally, the classification uses the walrus-optimized bidirectional long short-term memory (WOBLSTM). The simulation outcomes demonstrated the effectiveness of our method over existing techniques, with a higher sensitivity of 99.89% for CKD prediction.</p>","PeriodicalId":101409,"journal":{"name":"Informatics for health & social care","volume":" ","pages":"1-21"},"PeriodicalIF":2.4,"publicationDate":"2026-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146000468","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Digital mapping of health-related quality of life research in non-communicable diseases: a topic modeling and bibliometric synthesis for policy and practice.","authors":"Richa Jha, Geeta Sachdeva","doi":"10.1080/17538157.2025.2610690","DOIUrl":"10.1080/17538157.2025.2610690","url":null,"abstract":"<p><p>Health-related quality of life (HrQoL) has emerged as a key metric in the management of non-communicable diseases (NCDs), reflecting the global shift from acute infectious conditions to chronic illnesses. This shift indicates a broader movement toward patient-centered care. Amidst global digital health transformations, a synthesized understanding of the HrQoL research landscape is essential. To address this gap, the study adopts the PRISMA framework to present a combined bibliometric and machine learning-based Latent Dirichlet Allocation (LDA) topic modeling analysis of 353 articles, retrieved from Scopus and Web of Science between 2015 and 2024. Using R Studio and VOSviewer, the study maps publication trends, influential authors, sources, and countries, revealing an expanded research interest across related disciplines. Five key research topics were identified: interventions and disease management, psychosocial wellbeing, digital health technologies, epidemiological research, and preventive healthcare. These represent a thematic evolution from clinical protocols to more integrative and digitally responsive models. The findings highlight the need for culturally sensitive, interdisciplinary approaches that bridge clinical care with social determinants of health. Through its computational approach, the study offers actionable insights for researchers, clinicians, and policymakers seeking to enhance the quality of life of individuals living with chronic conditions.</p>","PeriodicalId":101409,"journal":{"name":"Informatics for health & social care","volume":" ","pages":"1-21"},"PeriodicalIF":2.4,"publicationDate":"2026-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145936891","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}